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Predicting Student Performance Using Classification Data Mining Techniques
Open Access   Article

Predicting Student Performance Using Classification Data Mining Techniques
Isha Shingari1 , Dinesh Kumar2

Section:Research Paper, Product Type: Journal Paper
Volume-6 , Issue-7 , Page no. 43-48, Jul-2018

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v6i7.4348

Online published on Jul 31, 2018

Copyright © Isha Shingari, Dinesh Kumar . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 
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IEEE Style Citation: Isha Shingari, Dinesh Kumar, “Predicting Student Performance Using Classification Data Mining Techniques”, International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.43-48, 2018.

MLA Style Citation: Isha Shingari, Dinesh Kumar "Predicting Student Performance Using Classification Data Mining Techniques." International Journal of Computer Sciences and Engineering 6.7 (2018): 43-48.

APA Style Citation: Isha Shingari, Dinesh Kumar, (2018). Predicting Student Performance Using Classification Data Mining Techniques. International Journal of Computer Sciences and Engineering, 6(7), 43-48.
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Abstract :
The term education data mining deals with extracting knowledge out of academic database which can be used for providing suitable patterns to education managers, teachers, and students. Education is a progressing field and students need to put in extra efforts to keep the right move towards learning. This paper presents on approach to study the student data and implementing various data mining classification algorithms. Thus, finding out the best algorithm, that can help in evaluating the final grade of a student and finding the best fit for identification of possible results beforehand, so that appropriate interventions can be planned. For our research we collected the data from a reputed higher education institute related to a set of students pertaining to their current and previous academic records. The data were filtered, cleaned, and processed for training different data mining models to define classifications based on different criteria. This method may be considered useful in finding out the students who are at the state of high risk in a very early stage, thus allowing the educationists to provide the appropriate advice to learners in a timely manner.
Key-Words / Index Term :
Education Data Mining, academic intervention, Data Classification, pattern identification
References :
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